Lou Ruvo Center for Brain Health, Cleveland Clinic, Las Vegas, NV, United States.
Biogen, Cambridge, MA, United States.
Mult Scler Relat Disord. 2024 Nov;91:105847. doi: 10.1016/j.msard.2024.105847. Epub 2024 Sep 2.
Two-stage models of heterogenous treatment effects (HTE) may advance personalized medicine in multiple sclerosis (MS). Brain atrophy is a relatively objective outcome measure that has strong relationships to MS prognosis and treatment effects and is enabled by standardized MRI.
To predict brain atrophy outcomes for patients initiating disease-modifying therapies (DMT) with different efficacies, considering the patients' baseline brain atrophy risk measured via brain parenchymal fraction (BPF).
Analyses included patients enrolled in the Multiple Sclerosis Partners Advancing Technology and Health Solutions (MS PATHS) network who started DMT and had complete baseline data and ≥ 6-month brain MRI follow-up. All brain MRIs were acquired using standardized acquisition sequences on Siemens 3T scanners. BPF change risk was derived by linear mixed effects models using baseline covariates. Model performance was assessed by predicted versus actual BPF change R. Propensity score (PS) weighting was used to balance covariates between groups defined by DMT efficacy (high: natalizumab, alemtuzumab, ocrelizumab, and rituximab; moderate: dimethyl fumarate, fingolimod, and cladribine; low: teriflunomide, interferons, and glatiramer acetate). HTE models predicting 1 year change in BPF were built using a weighted linear mixed effects model with low-efficacy DMT as the reference.
Analyses included 581 high-, 183 moderate-, and 106 low-efficacy DMT-treated patients. The mean and median number of brain MRI observations per treatment period were 2.9 and 3.0, respectively. Risk model performance R=0.55. After PS weighting, covariate standardized mean differences were <10 %, indicating excellent balance across measured variables. Changes in BPF between baseline and follow-up were found to be statistically significant (p < 0.001), suggesting a pathological change. Patients with low brain atrophy risk had a similar outcome regardless of DMT selection. In patients with high brain atrophy risk, high- and moderate-efficacy DMTs performed similarly, while a 2-fold worse BPF change was predicted for patients selecting low-efficacy DMTs (p < 0.001). Similar results were observed in a sensitivity analysis adjusting for pseudoatrophy effects in a sub-population of patients treated with natalizumab.
The relative benefit of selecting higher efficacy treatments may vary depending on patients' baseline brain atrophy risk. Poor outcomes are predicted in individuals with high baseline risk who are treated with low-efficacy DMTs.
两阶段异质治疗效果（HTE）模型可能会推进多发性硬化症（MS）的个性化医疗。脑萎缩是一种相对客观的结果测量方法，与 MS 的预后和治疗效果密切相关，并且可以通过标准化 MRI 实现。
考虑到通过脑实质分数（BPF）测量的患者基线脑萎缩风险，预测开始具有不同疗效的疾病修饰疗法（DMT）的患者的脑萎缩结果。
分析纳入了多发性硬化症合作伙伴推进技术和健康解决方案（MS PATHS）网络中开始 DMT 且具有完整基线数据和≥6 个月脑 MRI 随访的患者。所有脑 MRI 均使用西门子 3T 扫描仪上的标准化采集序列采集。BPF 变化风险通过线性混合效应模型使用基线协变量得出。通过预测与实际 BPF 变化 R 的比值来评估模型性能。使用倾向评分（PS）加权来平衡按 DMT 疗效（高：那他珠单抗、阿仑单抗、奥瑞珠单抗和利妥昔单抗；中：二甲基富马酸、芬戈莫德和克拉屈滨；低：特立氟胺、干扰素和聚乙二醇化干扰素）定义的组之间的协变量。使用加权线性混合效应模型，以低疗效 DMT 作为参考，建立预测 BPF 1 年变化的 HTE 模型。
分析纳入了 581 例高疗效、183 例中疗效和 106 例低疗效 DMT 治疗的患者。每个治疗期的平均和中位数脑 MRI 观察次数分别为 2.9 和 3.0。风险模型性能 R=0.55。经过 PS 加权后，协变量标准化均差<10%，表明在测量变量之间具有出色的平衡。基线和随访之间的 BPF 变化具有统计学意义（p<0.001），表明存在病理性变化。无论 DMT 的选择如何，低脑萎缩风险的患者的结果相似。在高脑萎缩风险的患者中，高疗效和中疗效 DMT 的效果相似，而选择低疗效 DMT 的患者的 BPF 变化预计会恶化两倍（p<0.001）。在亚组接受那他珠单抗治疗的患者中调整伪萎缩效应的敏感性分析中观察到了类似的结果。
根据患者的基线脑萎缩风险，选择更高疗效治疗的相对益处可能会有所不同。基线风险较高且接受低疗效 DMT 治疗的个体预后较差。
